FIG. 1 is a block diagram of a hypothetical image processing system utilizing pre-processing of image data prior to a compression and a post-processing process to reverse the pre-processing after decompression/expansion of the compressed data. In known image processing systems and methods, non-linear transformations of image data have been used in pre-processing steps prior to image compression. For example, some known systems utilize Anscombe and Homomorphic transforms or a lookup table (such as in U.S. Pat. No. 5,859,927, incorporated by reference). Other known techniques are disclosed in: (1) Osama K. Al-Shaykh, and Russell M. Mersereau, “Lossy Compression of Noisy Images,” IEEE Transactions on Image Processing, 1998; (2) Lukin, Zriakhov, Ponomarenko, Kaarna, “An Automatic Approach to Lossy Compression of Images Corrupted by Poisson Noise,” MRRS-2008 Symposium Proceedings, Kiev, Ukraine, 2008; (3) Shahnaz, Walkup, Krile, “Image Compression in signal-dependent noise,” Applied Optics, 1999; and (4) Nicula, Berghmans, Hochedez, “Poisson Recoding Of Solar Images For Enhanced Compression,” Solar Physics 2005. Each of those references is incorporated herein by reference.
A known alternative pre-processing step is known as “bit trimming.” Bit trimming reduces the number of digital values to be encoded, allowing better compression, by performing a simple divide by a power of 2, which discards the least significant bits of the (image) data. While bit trimming reduces the data load, it does not preserve the statistically significant data as well as possible.
As shown in FIG. 1, an optional delta quantizer and an optional inverse delta quantizer may be used independently of the pre-processing to provide an alternate form of compression. In such a configuration, the delta quantizer applies to the compression circuitry the delta between a predicted value to be encoded and the actual value output from the pre-processor, and the inverse delta quantizer performs the inverse process during decompression.